Overview

Dataset statistics

Number of variables44
Number of observations12023
Missing cells18849
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory338.0 B

Variable types

Categorical23
DateTime1
Numeric18
Boolean2

Warnings

game_id has a high cardinality: 12023 distinct values High cardinality
date_string has a high cardinality: 2301 distinct values High cardinality
over_under_line has a high cardinality: 68 distinct values High cardinality
stadium has a high cardinality: 101 distinct values High cardinality
address has a high cardinality: 89 distinct values High cardinality
qb1 has a high cardinality: 527 distinct values High cardinality
qb2 has a high cardinality: 540 distinct values High cardinality
elo1_pre is highly correlated with qbelo1_preHigh correlation
elo2_pre is highly correlated with qbelo2_preHigh correlation
elo_prob1 is highly correlated with elo_prob2 and 2 other fieldsHigh correlation
elo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo1_pre is highly correlated with elo1_preHigh correlation
qbelo2_pre is highly correlated with elo2_preHigh correlation
qbelo_prob1 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
away_team_id is highly correlated with away_teamname and 3 other fieldsHigh correlation
home_city is highly correlated with team1 and 4 other fieldsHigh correlation
compass_home is highly correlated with stadium_neutralHigh correlation
team1 is highly correlated with home_city and 4 other fieldsHigh correlation
schedule_week is highly correlated with schedule_playoffHigh correlation
home_team_id is highly correlated with home_city and 4 other fieldsHigh correlation
away_teamname is highly correlated with away_team_id and 3 other fieldsHigh correlation
home_teamname is highly correlated with home_city and 4 other fieldsHigh correlation
team_home is highly correlated with home_city and 4 other fieldsHigh correlation
team2 is highly correlated with away_team_id and 3 other fieldsHigh correlation
stadium_neutral is highly correlated with compass_homeHigh correlation
away_city is highly correlated with away_team_id and 3 other fieldsHigh correlation
schedule_playoff is highly correlated with schedule_weekHigh correlation
address is highly correlated with home_city and 4 other fieldsHigh correlation
team_away is highly correlated with away_team_id and 3 other fieldsHigh correlation
team_favorite_id has 2289 (19.0%) missing values Missing
spread_favorite has 2289 (19.0%) missing values Missing
over_under_line has 2298 (19.1%) missing values Missing
compass_home has 11973 (99.6%) missing values Missing
dt_for_home is highly skewed (γ1 = 23.37250991) Skewed
bearing_home is highly skewed (γ1 = 23.58029354) Skewed
game_id is uniformly distributed Uniform
game_id has unique values Unique
qbelo_prob1 has unique values Unique
qbelo_prob2 has unique values Unique
score_home has 185 (1.5%) zeros Zeros
score_away has 315 (2.6%) zeros Zeros
spread_favorite has 133 (1.1%) zeros Zeros
dt_for_home has 11973 (99.6%) zeros Zeros
bearing_home has 11973 (99.6%) zeros Zeros

Reproduction

Analysis started2021-04-16 03:38:05.029684
Analysis finished2021-04-16 03:38:48.428442
Duration43.4 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

game_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct12023
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
20201213CARDEN
 
1
20131215MINPHI
 
1
19681128KCTEN
 
1
20091004NEBAL
 
1
19711031INDPIT
 
1
Other values (12018)
12018 

Length

Max length14
Median length14
Mean length13.58388089
Min length12

Characters and Unicode

Total characters163319
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12023 ?
Unique (%)100.0%

Sample

1st row19660902MIALVR
2nd row19660903TENDEN
3rd row19660904LACBUF
4th row19660909MIANYJ
5th row19660910GBIND
ValueCountFrequency (%)
20201213CARDEN1
 
< 0.1%
20131215MINPHI1
 
< 0.1%
19681128KCTEN1
 
< 0.1%
20091004NEBAL1
 
< 0.1%
19711031INDPIT1
 
< 0.1%
20041010INDLVR1
 
< 0.1%
19950924CLEKC1
 
< 0.1%
19910908DETGB1
 
< 0.1%
20040104INDDEN1
 
< 0.1%
20120927BALCLE1
 
< 0.1%
Other values (12013)12013
99.9%
2021-04-15T23:38:48.650892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20140907pitcle1
 
< 0.1%
19701018bufmia1
 
< 0.1%
19870913mindet1
 
< 0.1%
20201108dalpit1
 
< 0.1%
19871129nyjcin1
 
< 0.1%
19950107pitcle1
 
< 0.1%
20111106dalsea1
 
< 0.1%
20011223nygsea1
 
< 0.1%
20121007nolac1
 
< 0.1%
20040926denlac1
 
< 0.1%
Other values (12013)12013
99.9%

Most occurring characters

ValueCountFrequency (%)
128387
17.4%
019804
 
12.1%
214611
 
8.9%
914041
 
8.6%
N7619
 
4.7%
A6964
 
4.3%
I6697
 
4.1%
L5437
 
3.3%
E4968
 
3.0%
C4609
 
2.8%
Other values (23)50182
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number96184
58.9%
Uppercase Letter67135
41.1%

Most frequent character per category

ValueCountFrequency (%)
N7619
11.3%
A6964
 
10.4%
I6697
 
10.0%
L5437
 
8.1%
E4968
 
7.4%
C4609
 
6.9%
T4112
 
6.1%
D3356
 
5.0%
R2923
 
4.4%
B2847
 
4.2%
Other values (13)17603
26.2%
ValueCountFrequency (%)
128387
29.5%
019804
20.6%
214611
15.2%
914041
14.6%
84550
 
4.7%
74294
 
4.5%
63144
 
3.3%
32858
 
3.0%
42290
 
2.4%
52205
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common96184
58.9%
Latin67135
41.1%

Most frequent character per script

ValueCountFrequency (%)
N7619
11.3%
A6964
 
10.4%
I6697
 
10.0%
L5437
 
8.1%
E4968
 
7.4%
C4609
 
6.9%
T4112
 
6.1%
D3356
 
5.0%
R2923
 
4.4%
B2847
 
4.2%
Other values (13)17603
26.2%
ValueCountFrequency (%)
128387
29.5%
019804
20.6%
214611
15.2%
914041
14.6%
84550
 
4.7%
74294
 
4.5%
63144
 
3.3%
32858
 
3.0%
42290
 
2.4%
52205
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII163319
100.0%

Most frequent character per block

ValueCountFrequency (%)
128387
17.4%
019804
 
12.1%
214611
 
8.9%
914041
 
8.6%
N7619
 
4.7%
A6964
 
4.3%
I6697
 
4.1%
L5437
 
3.3%
E4968
 
3.0%
C4609
 
2.8%
Other values (23)50182
30.7%

date_string
Categorical

HIGH CARDINALITY

Distinct2301
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Sun Jan 01, 2017
 
15
Sun Jan 03, 2010
 
15
Sun Jan 03, 2016
 
15
Sun Sep 16, 2007
 
15
Sun Dec 28, 2008
 
15
Other values (2296)
11948 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters192368
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1070 ?
Unique (%)8.9%

Sample

1st rowFri Sep 02, 1966
2nd rowSat Sep 03, 1966
3rd rowSun Sep 04, 1966
4th rowFri Sep 09, 1966
5th rowSat Sep 10, 1966
ValueCountFrequency (%)
Sun Jan 01, 201715
 
0.1%
Sun Jan 03, 201015
 
0.1%
Sun Jan 03, 201615
 
0.1%
Sun Sep 16, 200715
 
0.1%
Sun Dec 28, 200815
 
0.1%
Sun Dec 30, 201215
 
0.1%
Sun Jan 02, 200515
 
0.1%
Sun Dec 30, 201815
 
0.1%
Sun Jan 01, 201215
 
0.1%
Sun Jan 03, 202115
 
0.1%
Other values (2291)11873
98.8%
2021-04-15T23:38:48.900639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sun10488
21.8%
nov3076
 
6.4%
dec2940
 
6.1%
oct2936
 
6.1%
sep2516
 
5.2%
mon754
 
1.6%
jan531
 
1.1%
sat436
 
0.9%
20432
 
0.9%
15429
 
0.9%
Other values (91)23554
49.0%

Most occurring characters

ValueCountFrequency (%)
36069
18.8%
115828
 
8.2%
013797
 
7.2%
S13440
 
7.0%
,12023
 
6.2%
n11773
 
6.1%
211660
 
6.1%
911525
 
6.0%
u10828
 
5.6%
c5876
 
3.1%
Other values (27)49549
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number72138
37.5%
Lowercase Letter48092
25.0%
Space Separator36069
18.8%
Uppercase Letter24046
 
12.5%
Other Punctuation12023
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
n11773
24.5%
u10828
22.5%
c5876
12.2%
e5472
11.4%
o3830
 
8.0%
t3372
 
7.0%
v3076
 
6.4%
p2516
 
5.2%
a967
 
2.0%
h324
 
0.7%
Other values (5)58
 
0.1%
ValueCountFrequency (%)
S13440
55.9%
N3076
 
12.8%
D2940
 
12.2%
O2936
 
12.2%
M754
 
3.1%
J531
 
2.2%
T327
 
1.4%
F27
 
0.1%
A13
 
0.1%
W2
 
< 0.1%
ValueCountFrequency (%)
115828
21.9%
013797
19.1%
211660
16.2%
911525
16.0%
84537
 
6.3%
74294
 
6.0%
63144
 
4.4%
32858
 
4.0%
42290
 
3.2%
52205
 
3.1%
ValueCountFrequency (%)
36069
100.0%
ValueCountFrequency (%)
,12023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common120230
62.5%
Latin72138
37.5%

Most frequent character per script

ValueCountFrequency (%)
S13440
18.6%
n11773
16.3%
u10828
15.0%
c5876
8.1%
e5472
7.6%
o3830
 
5.3%
t3372
 
4.7%
N3076
 
4.3%
v3076
 
4.3%
D2940
 
4.1%
Other values (15)8455
11.7%
ValueCountFrequency (%)
36069
30.0%
115828
13.2%
013797
 
11.5%
,12023
 
10.0%
211660
 
9.7%
911525
 
9.6%
84537
 
3.8%
74294
 
3.6%
63144
 
2.6%
32858
 
2.4%
Other values (2)4495
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII192368
100.0%

Most frequent character per block

ValueCountFrequency (%)
36069
18.8%
115828
 
8.2%
013797
 
7.2%
S13440
 
7.0%
,12023
 
6.2%
n11773
 
6.1%
211660
 
6.1%
911525
 
6.0%
u10828
 
5.6%
c5876
 
3.1%
Other values (27)49549
25.8%
Distinct2301
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Minimum1966-09-02 00:00:00
Maximum2021-02-07 00:00:00
2021-04-15T23:38:49.003100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:49.134824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

schedule_season
Real number (ℝ≥0)

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.066123
Minimum1966
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:49.272160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1966
5-th percentile1969
Q11982
median1996
Q32008
95-th percentile2018
Maximum2020
Range54
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.5556659
Coefficient of variation (CV)0.007797067837
Kurtosis-1.136110711
Mean1995.066123
Median Absolute Deviation (MAD)13
Skewness-0.1615152346
Sum23986680
Variance241.9787416
MonotocityIncreasing
2021-04-15T23:38:49.405367image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020252
 
2.1%
2019251
 
2.1%
2017251
 
2.1%
2006251
 
2.1%
2004251
 
2.1%
2018251
 
2.1%
2003250
 
2.1%
2002250
 
2.1%
2015250
 
2.1%
2016250
 
2.1%
Other values (45)9516
79.1%
ValueCountFrequency (%)
1966156
1.3%
1967165
1.4%
1968173
1.4%
1969174
1.4%
1970174
1.4%
ValueCountFrequency (%)
2020252
2.1%
2019251
2.1%
2018251
2.1%
2017251
2.1%
2016250
2.1%

schedule_week
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
13
 
746
2
 
744
14
 
743
12
 
739
1
 
739
Other values (18)
8312 

Length

Max length10
Median length1
Mean length1.738501206
Min length1

Characters and Unicode

Total characters20902
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1
ValueCountFrequency (%)
13746
 
6.2%
2744
 
6.2%
14743
 
6.2%
12739
 
6.1%
1739
 
6.1%
11727
 
6.0%
3698
 
5.8%
10693
 
5.8%
9676
 
5.6%
4674
 
5.6%
Other values (13)4844
40.3%
2021-04-15T23:38:49.633240image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13746
 
6.2%
2744
 
6.2%
14743
 
6.2%
12739
 
6.1%
1739
 
6.1%
11727
 
6.0%
3698
 
5.8%
10693
 
5.8%
9676
 
5.6%
4674
 
5.6%
Other values (12)4844
40.3%

Most occurring characters

ValueCountFrequency (%)
16827
32.7%
21483
 
7.1%
31444
 
6.9%
41417
 
6.8%
51290
 
6.2%
61270
 
6.1%
71134
 
5.4%
i727
 
3.5%
0693
 
3.3%
8681
 
3.3%
Other values (20)3936
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16915
80.9%
Lowercase Letter3514
 
16.8%
Uppercase Letter473
 
2.3%

Most frequent character per category

ValueCountFrequency (%)
i727
20.7%
n404
11.5%
e340
9.7%
o324
9.2%
d290
 
8.3%
r275
 
7.8%
c246
 
7.0%
v194
 
5.5%
s194
 
5.5%
l170
 
4.8%
Other values (6)350
10.0%
ValueCountFrequency (%)
16827
40.4%
21483
 
8.8%
31444
 
8.5%
41417
 
8.4%
51290
 
7.6%
61270
 
7.5%
71134
 
6.7%
0693
 
4.1%
8681
 
4.0%
9676
 
4.0%
ValueCountFrequency (%)
D194
41.0%
W145
30.7%
C109
23.0%
S25
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common16915
80.9%
Latin3987
 
19.1%

Most frequent character per script

ValueCountFrequency (%)
i727
18.2%
n404
10.1%
e340
8.5%
o324
8.1%
d290
 
7.3%
r275
 
6.9%
c246
 
6.2%
D194
 
4.9%
v194
 
4.9%
s194
 
4.9%
Other values (10)799
20.0%
ValueCountFrequency (%)
16827
40.4%
21483
 
8.8%
31444
 
8.5%
41417
 
8.4%
51290
 
7.6%
61270
 
7.5%
71134
 
6.7%
0693
 
4.1%
8681
 
4.0%
9676
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20902
100.0%

Most frequent character per block

ValueCountFrequency (%)
16827
32.7%
21483
 
7.1%
31444
 
6.9%
41417
 
6.8%
51290
 
6.2%
61270
 
6.1%
71134
 
5.4%
i727
 
3.5%
0693
 
3.3%
8681
 
3.3%
Other values (20)3936
18.8%

schedule_playoff
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
False
11551 
True
 
472
ValueCountFrequency (%)
False11551
96.1%
True472
 
3.9%
2021-04-15T23:38:49.692976image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

home_team_id
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
NE
 
445
PIT
 
443
SF
 
442
DEN
 
441
LVR
 
440
Other values (26)
9812 

Length

Max length3
Median length3
Mean length2.791067121
Min length2

Characters and Unicode

Total characters33557
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIA
2nd rowTEN
3rd rowLAC
4th rowMIA
5th rowGB
ValueCountFrequency (%)
NE445
 
3.7%
PIT443
 
3.7%
SF442
 
3.7%
DEN441
 
3.7%
LVR440
 
3.7%
MIA437
 
3.6%
GB436
 
3.6%
KC433
 
3.6%
IND431
 
3.6%
MIN431
 
3.6%
Other values (21)7644
63.6%
2021-04-15T23:38:49.873182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne445
 
3.7%
pit443
 
3.7%
sf442
 
3.7%
den441
 
3.7%
lvr440
 
3.7%
mia437
 
3.6%
gb436
 
3.6%
kc433
 
3.6%
ind431
 
3.6%
min431
 
3.6%
Other values (21)7644
63.6%

Most occurring characters

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter33557
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

Most occurring scripts

ValueCountFrequency (%)
Latin33557
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33557
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

home_city
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
New York
 
806
Pittsburgh
 
443
San Francisco
 
442
Denver
 
441
Miami
 
437
Other values (28)
9454 

Length

Max length13
Median length9
Mean length8.823089079
Min length5

Characters and Unicode

Total characters106080
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiami
2nd rowHouston
3rd rowSan Diego
4th rowMiami
5th rowGreen Bay
ValueCountFrequency (%)
New York806
 
6.7%
Pittsburgh443
 
3.7%
San Francisco442
 
3.7%
Denver441
 
3.7%
Miami437
 
3.6%
Green Bay436
 
3.6%
Kansas City433
 
3.6%
Minneapolis431
 
3.6%
Buffalo428
 
3.6%
Chicago422
 
3.5%
Other values (23)7304
60.8%
2021-04-15T23:38:50.182666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1217
 
7.6%
san840
 
5.3%
york806
 
5.0%
bay781
 
4.9%
pittsburgh443
 
2.8%
francisco442
 
2.8%
denver441
 
2.8%
miami437
 
2.7%
green436
 
2.7%
city433
 
2.7%
Other values (30)9722
60.8%

Most occurring characters

ValueCountFrequency (%)
a11361
 
10.7%
e9088
 
8.6%
n8509
 
8.0%
i8002
 
7.5%
o7415
 
7.0%
l6403
 
6.0%
t5587
 
5.3%
s5416
 
5.1%
r4345
 
4.1%
3975
 
3.7%
Other values (34)35979
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter85803
80.9%
Uppercase Letter15998
 
15.1%
Space Separator3975
 
3.7%
Other Punctuation304
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
a11361
13.2%
e9088
10.6%
n8509
9.9%
i8002
9.3%
o7415
8.6%
l6403
 
7.5%
t5587
 
6.5%
s5416
 
6.3%
r4345
 
5.1%
h2512
 
2.9%
Other values (13)17165
20.0%
ValueCountFrequency (%)
C1875
11.7%
D1652
10.3%
B1578
9.9%
S1502
 
9.4%
N1217
 
7.6%
P1081
 
6.8%
M868
 
5.4%
F853
 
5.3%
A810
 
5.1%
Y806
 
5.0%
Other values (9)3756
23.5%
ValueCountFrequency (%)
3975
100.0%
ValueCountFrequency (%)
.304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin101801
96.0%
Common4279
 
4.0%

Most frequent character per script

ValueCountFrequency (%)
a11361
 
11.2%
e9088
 
8.9%
n8509
 
8.4%
i8002
 
7.9%
o7415
 
7.3%
l6403
 
6.3%
t5587
 
5.5%
s5416
 
5.3%
r4345
 
4.3%
h2512
 
2.5%
Other values (32)33163
32.6%
ValueCountFrequency (%)
3975
92.9%
.304
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII106080
100.0%

Most frequent character per block

ValueCountFrequency (%)
a11361
 
10.7%
e9088
 
8.6%
n8509
 
8.0%
i8002
 
7.5%
o7415
 
7.0%
l6403
 
6.0%
t5587
 
5.3%
s5416
 
5.1%
r4345
 
4.1%
3975
 
3.7%
Other values (34)35979
33.9%

home_teamname
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Patriots
 
445
Steelers
 
443
49ers
 
442
Broncos
 
441
Raiders
 
440
Other values (27)
9812 

Length

Max length10
Median length7
Mean length6.543874241
Min length4

Characters and Unicode

Total characters78677
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDolphins
2nd rowOilers
3rd rowChargers
4th rowDolphins
5th rowPackers
ValueCountFrequency (%)
Patriots445
 
3.7%
Steelers443
 
3.7%
49ers442
 
3.7%
Broncos441
 
3.7%
Raiders440
 
3.7%
Dolphins437
 
3.6%
Packers436
 
3.6%
Chiefs433
 
3.6%
Colts431
 
3.6%
Vikings431
 
3.6%
Other values (22)7644
63.6%
2021-04-15T23:38:50.405794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
patriots445
 
3.7%
steelers443
 
3.7%
49ers442
 
3.7%
broncos441
 
3.7%
raiders440
 
3.7%
dolphins437
 
3.6%
packers436
 
3.6%
chiefs433
 
3.6%
vikings431
 
3.6%
colts431
 
3.6%
Other values (22)7644
63.6%

Most occurring characters

ValueCountFrequency (%)
s12023
15.3%
a7604
 
9.7%
e7023
 
8.9%
r5720
 
7.3%
n5225
 
6.6%
i5065
 
6.4%
o4226
 
5.4%
l4023
 
5.1%
t3367
 
4.3%
B2448
 
3.1%
Other values (29)21953
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter66212
84.2%
Uppercase Letter11581
 
14.7%
Decimal Number884
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
s12023
18.2%
a7604
11.5%
e7023
10.6%
r5720
8.6%
n5225
7.9%
i5065
7.6%
o4226
 
6.4%
l4023
 
6.1%
t3367
 
5.1%
c1985
 
3.0%
Other values (13)9951
15.0%
ValueCountFrequency (%)
B2448
21.1%
C2080
18.0%
S1212
10.5%
P1089
9.4%
R1069
9.2%
J628
 
5.4%
D437
 
3.8%
V431
 
3.7%
F418
 
3.6%
L412
 
3.6%
Other values (4)1357
11.7%
ValueCountFrequency (%)
4442
50.0%
9442
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin77793
98.9%
Common884
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
s12023
15.5%
a7604
 
9.8%
e7023
 
9.0%
r5720
 
7.4%
n5225
 
6.7%
i5065
 
6.5%
o4226
 
5.4%
l4023
 
5.2%
t3367
 
4.3%
B2448
 
3.1%
Other values (27)21069
27.1%
ValueCountFrequency (%)
4442
50.0%
9442
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII78677
100.0%

Most frequent character per block

ValueCountFrequency (%)
s12023
15.3%
a7604
 
9.7%
e7023
 
8.9%
r5720
 
7.3%
n5225
 
6.6%
i5065
 
6.4%
o4226
 
5.4%
l4023
 
5.1%
t3367
 
4.3%
B2448
 
3.1%
Other values (29)21953
27.9%

away_city
Categorical

HIGH CORRELATION

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
New York
 
814
Pittsburgh
 
438
Green Bay
 
436
Kansas City
 
434
Minneapolis
 
432
Other values (28)
9469 

Length

Max length13
Median length9
Mean length8.834317558
Min length5

Characters and Unicode

Total characters106215
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOakland
2nd rowDenver
3rd rowBuffalo
4th rowNew York
5th rowBaltimore
ValueCountFrequency (%)
New York814
 
6.8%
Pittsburgh438
 
3.6%
Green Bay436
 
3.6%
Kansas City434
 
3.6%
Minneapolis432
 
3.6%
Buffalo431
 
3.6%
Denver430
 
3.6%
Foxborough430
 
3.6%
Miami429
 
3.6%
Atlanta422
 
3.5%
Other values (23)7327
60.9%
2021-04-15T23:38:50.638274image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1225
 
7.7%
san815
 
5.1%
york814
 
5.1%
bay788
 
4.9%
pittsburgh438
 
2.7%
green436
 
2.7%
city434
 
2.7%
kansas434
 
2.7%
minneapolis432
 
2.7%
buffalo431
 
2.7%
Other values (30)9755
61.0%

Most occurring characters

ValueCountFrequency (%)
a11330
 
10.7%
e9155
 
8.6%
n8480
 
8.0%
i7977
 
7.5%
o7448
 
7.0%
l6428
 
6.1%
t5582
 
5.3%
s5411
 
5.1%
r4346
 
4.1%
3979
 
3.7%
Other values (34)36079
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter85927
80.9%
Uppercase Letter16002
 
15.1%
Space Separator3979
 
3.7%
Other Punctuation307
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
a11330
13.2%
e9155
10.7%
n8480
9.9%
i7977
9.3%
o7448
8.7%
l6428
 
7.5%
t5582
 
6.5%
s5411
 
6.3%
r4346
 
5.1%
h2516
 
2.9%
Other values (13)17254
20.1%
ValueCountFrequency (%)
C1877
11.7%
D1628
10.2%
B1569
9.8%
S1482
 
9.3%
N1225
 
7.7%
P1074
 
6.7%
M861
 
5.4%
F851
 
5.3%
A824
 
5.1%
Y814
 
5.1%
Other values (9)3797
23.7%
ValueCountFrequency (%)
3979
100.0%
ValueCountFrequency (%)
.307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin101929
96.0%
Common4286
 
4.0%

Most frequent character per script

ValueCountFrequency (%)
a11330
 
11.1%
e9155
 
9.0%
n8480
 
8.3%
i7977
 
7.8%
o7448
 
7.3%
l6428
 
6.3%
t5582
 
5.5%
s5411
 
5.3%
r4346
 
4.3%
h2516
 
2.5%
Other values (32)33256
32.6%
ValueCountFrequency (%)
3979
92.8%
.307
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII106215
100.0%

Most frequent character per block

ValueCountFrequency (%)
a11330
 
10.7%
e9155
 
8.6%
n8480
 
8.0%
i7977
 
7.5%
o7448
 
7.0%
l6428
 
6.1%
t5582
 
5.3%
s5411
 
5.1%
r4346
 
4.1%
3979
 
3.7%
Other values (34)36079
34.0%

away_teamname
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Steelers
 
438
Colts
 
437
Patriots
 
437
Packers
 
436
Jets
 
434
Other values (27)
9841 

Length

Max length10
Median length7
Mean length6.540796806
Min length4

Characters and Unicode

Total characters78640
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRaiders
2nd rowBroncos
3rd rowBills
4th rowJets
5th rowColts
ValueCountFrequency (%)
Steelers438
 
3.6%
Colts437
 
3.6%
Patriots437
 
3.6%
Packers436
 
3.6%
Jets434
 
3.6%
Chiefs434
 
3.6%
Rams433
 
3.6%
Vikings432
 
3.6%
Bills431
 
3.6%
Raiders431
 
3.6%
Other values (22)7680
63.9%
2021-04-15T23:38:50.858025image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
steelers438
 
3.6%
patriots437
 
3.6%
colts437
 
3.6%
packers436
 
3.6%
chiefs434
 
3.6%
jets434
 
3.6%
rams433
 
3.6%
vikings432
 
3.6%
raiders431
 
3.6%
bills431
 
3.6%
Other values (22)7680
63.9%

Most occurring characters

ValueCountFrequency (%)
s12023
15.3%
a7641
 
9.7%
e7008
 
8.9%
r5696
 
7.2%
n5239
 
6.7%
i5065
 
6.4%
o4183
 
5.3%
l4034
 
5.1%
t3368
 
4.3%
B2447
 
3.1%
Other values (29)21936
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter66196
84.2%
Uppercase Letter11602
 
14.8%
Decimal Number842
 
1.1%

Most frequent character per category

ValueCountFrequency (%)
s12023
18.2%
a7641
11.5%
e7008
10.6%
r5696
8.6%
n5239
7.9%
i5065
7.7%
o4183
 
6.3%
l4034
 
6.1%
t3368
 
5.1%
c1992
 
3.0%
Other values (13)9947
15.0%
ValueCountFrequency (%)
B2447
21.1%
C2082
17.9%
S1209
10.4%
P1082
9.3%
R1077
9.3%
J649
 
5.6%
V432
 
3.7%
D429
 
3.7%
F422
 
3.6%
L414
 
3.6%
Other values (4)1359
11.7%
ValueCountFrequency (%)
4421
50.0%
9421
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin77798
98.9%
Common842
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
s12023
15.5%
a7641
 
9.8%
e7008
 
9.0%
r5696
 
7.3%
n5239
 
6.7%
i5065
 
6.5%
o4183
 
5.4%
l4034
 
5.2%
t3368
 
4.3%
B2447
 
3.1%
Other values (27)21094
27.1%
ValueCountFrequency (%)
4421
50.0%
9421
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII78640
100.0%

Most frequent character per block

ValueCountFrequency (%)
s12023
15.3%
a7641
 
9.7%
e7008
 
8.9%
r5696
 
7.2%
n5239
 
6.7%
i5065
 
6.4%
o4183
 
5.3%
l4034
 
5.1%
t3368
 
4.3%
B2447
 
3.1%
Other values (29)21936
27.9%

away_team_id
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
TEN
 
441
PIT
 
438
IND
 
437
NE
 
437
GB
 
436
Other values (26)
9834 

Length

Max length3
Median length3
Mean length2.792813774
Min length2

Characters and Unicode

Total characters33578
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLVR
2nd rowDEN
3rd rowBUF
4th rowNYJ
5th rowIND
ValueCountFrequency (%)
TEN441
 
3.7%
PIT438
 
3.6%
IND437
 
3.6%
NE437
 
3.6%
GB436
 
3.6%
NYJ434
 
3.6%
KC434
 
3.6%
LAR433
 
3.6%
MIN432
 
3.6%
LVR431
 
3.6%
Other values (21)7670
63.8%
2021-04-15T23:38:51.061576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ten441
 
3.7%
pit438
 
3.6%
ne437
 
3.6%
ind437
 
3.6%
gb436
 
3.6%
nyj434
 
3.6%
kc434
 
3.6%
lar433
 
3.6%
min432
 
3.6%
buf431
 
3.6%
Other values (21)7670
63.8%

Most occurring characters

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter33578
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

Most occurring scripts

ValueCountFrequency (%)
Latin33578
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33578
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

result
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
1
6900 
0
5123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12023
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
16900
57.4%
05123
42.6%
2021-04-15T23:38:51.235185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-15T23:38:51.290831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
16900
57.4%
05123
42.6%

Most occurring characters

ValueCountFrequency (%)
16900
57.4%
05123
42.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12023
100.0%

Most frequent character per category

ValueCountFrequency (%)
16900
57.4%
05123
42.6%

Most occurring scripts

ValueCountFrequency (%)
Common12023
100.0%

Most frequent character per script

ValueCountFrequency (%)
16900
57.4%
05123
42.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII12023
100.0%

Most frequent character per block

ValueCountFrequency (%)
16900
57.4%
05123
42.6%

team_home
Categorical

HIGH CORRELATION

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Pittsburgh Steelers
 
443
San Francisco 49ers
 
442
Denver Broncos
 
441
Miami Dolphins
 
437
Green Bay Packers
 
436
Other values (36)
9824 

Length

Max length20
Median length16
Mean length16.31215171
Min length13

Characters and Unicode

Total characters196121
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiami Dolphins
2nd rowHouston Oilers
3rd rowSan Diego Chargers
4th rowMiami Dolphins
5th rowGreen Bay Packers
ValueCountFrequency (%)
Pittsburgh Steelers443
 
3.7%
San Francisco 49ers442
 
3.7%
Denver Broncos441
 
3.7%
Miami Dolphins437
 
3.6%
Green Bay Packers436
 
3.6%
Kansas City Chiefs433
 
3.6%
Minnesota Vikings431
 
3.6%
Buffalo Bills428
 
3.6%
New York Jets422
 
3.5%
Chicago Bears422
 
3.5%
Other values (31)7688
63.9%
2021-04-15T23:38:51.481226image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1628
 
5.7%
san840
 
3.0%
york806
 
2.8%
bay781
 
2.7%
patriots445
 
1.6%
pittsburgh443
 
1.6%
steelers443
 
1.6%
francisco442
 
1.6%
49ers442
 
1.6%
broncos441
 
1.6%
Other values (63)21721
76.4%

Most occurring characters

ValueCountFrequency (%)
a19790
 
10.1%
s17439
 
8.9%
16409
 
8.4%
e16108
 
8.2%
n14764
 
7.5%
i12844
 
6.5%
o10408
 
5.3%
l10406
 
5.3%
r9860
 
5.0%
t8969
 
4.6%
Other values (39)59124
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter150534
76.8%
Uppercase Letter27990
 
14.3%
Space Separator16409
 
8.4%
Decimal Number884
 
0.5%
Other Punctuation304
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
a19790
13.1%
s17439
11.6%
e16108
10.7%
n14764
9.8%
i12844
8.5%
o10408
6.9%
l10406
6.9%
r9860
 
6.6%
t8969
 
6.0%
g3943
 
2.6%
Other values (14)26003
17.3%
ValueCountFrequency (%)
B4026
14.4%
C3955
14.1%
S2714
 
9.7%
D2089
 
7.5%
P1964
 
7.0%
N1628
 
5.8%
L1116
 
4.0%
R1069
 
3.8%
A1016
 
3.6%
O988
 
3.5%
Other values (11)7425
26.5%
ValueCountFrequency (%)
4442
50.0%
9442
50.0%
ValueCountFrequency (%)
16409
100.0%
ValueCountFrequency (%)
.304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin178524
91.0%
Common17597
 
9.0%

Most frequent character per script

ValueCountFrequency (%)
a19790
 
11.1%
s17439
 
9.8%
e16108
 
9.0%
n14764
 
8.3%
i12844
 
7.2%
o10408
 
5.8%
l10406
 
5.8%
r9860
 
5.5%
t8969
 
5.0%
B4026
 
2.3%
Other values (35)53910
30.2%
ValueCountFrequency (%)
16409
93.2%
4442
 
2.5%
9442
 
2.5%
.304
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII196121
100.0%

Most frequent character per block

ValueCountFrequency (%)
a19790
 
10.1%
s17439
 
8.9%
16409
 
8.4%
e16108
 
8.2%
n14764
 
7.5%
i12844
 
6.5%
o10408
 
5.3%
l10406
 
5.3%
r9860
 
5.0%
t8969
 
4.6%
Other values (39)59124
30.1%

score_home
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.46435998
Minimum0
Maximum62
Zeros185
Zeros (%)1.5%
Memory size94.1 KiB
2021-04-15T23:38:51.592572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q114
median22
Q329
95-th percentile41
Maximum62
Range62
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.5741611
Coefficient of variation (CV)0.4707083179
Kurtosis0.01192391205
Mean22.46435998
Median Absolute Deviation (MAD)8
Skewness0.3240684165
Sum270089
Variance111.8128829
MonotocityNot monotonic
2021-04-15T23:38:51.711718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20841
 
7.0%
17833
 
6.9%
24814
 
6.8%
27703
 
5.8%
10584
 
4.9%
13565
 
4.7%
31562
 
4.7%
14557
 
4.6%
21504
 
4.2%
23496
 
4.1%
Other values (50)5564
46.3%
ValueCountFrequency (%)
0185
1.5%
24
 
< 0.1%
3228
1.9%
57
 
0.1%
6206
1.7%
ValueCountFrequency (%)
624
< 0.1%
612
 
< 0.1%
596
< 0.1%
586
< 0.1%
572
 
< 0.1%

score_away
Real number (ℝ≥0)

ZEROS

Distinct58
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.69059303
Minimum0
Maximum62
Zeros315
Zeros (%)2.6%
Memory size94.1 KiB
2021-04-15T23:38:51.835368image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q113
median20
Q327
95-th percentile38
Maximum62
Range62
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.19919314
Coefficient of variation (CV)0.5179728779
Kurtosis-0.1579705189
Mean19.69059303
Median Absolute Deviation (MAD)7
Skewness0.3351503968
Sum236740
Variance104.0235407
MonotocityNot monotonic
2021-04-15T23:38:52.041917image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17953
 
7.9%
10839
 
7.0%
20744
 
6.2%
24742
 
6.2%
14689
 
5.7%
13645
 
5.4%
7590
 
4.9%
21562
 
4.7%
27533
 
4.4%
23508
 
4.2%
Other values (48)5218
43.4%
ValueCountFrequency (%)
0315
2.6%
25
 
< 0.1%
3404
3.4%
59
 
0.1%
6298
2.5%
ValueCountFrequency (%)
621
 
< 0.1%
611
 
< 0.1%
592
< 0.1%
571
 
< 0.1%
563
< 0.1%

team_away
Categorical

HIGH CORRELATION

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Pittsburgh Steelers
 
438
Green Bay Packers
 
436
New York Jets
 
434
Kansas City Chiefs
 
434
Minnesota Vikings
 
432
Other values (36)
9849 

Length

Max length20
Median length16
Mean length16.32163354
Min length13

Characters and Unicode

Total characters196235
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOakland Raiders
2nd rowDenver Broncos
3rd rowBuffalo Bills
4th rowNew York Jets
5th rowBaltimore Colts
ValueCountFrequency (%)
Pittsburgh Steelers438
 
3.6%
Green Bay Packers436
 
3.6%
New York Jets434
 
3.6%
Kansas City Chiefs434
 
3.6%
Minnesota Vikings432
 
3.6%
Buffalo Bills431
 
3.6%
New England Patriots430
 
3.6%
Denver Broncos430
 
3.6%
Miami Dolphins429
 
3.6%
Atlanta Falcons422
 
3.5%
Other values (31)7707
64.1%
2021-04-15T23:38:52.301541image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new1655
 
5.8%
san815
 
2.9%
york814
 
2.9%
bay788
 
2.8%
steelers438
 
1.5%
pittsburgh438
 
1.5%
patriots437
 
1.5%
colts437
 
1.5%
packers436
 
1.5%
green436
 
1.5%
Other values (63)21761
76.5%

Most occurring characters

ValueCountFrequency (%)
a19819
 
10.1%
s17434
 
8.9%
16432
 
8.4%
e16175
 
8.2%
n14788
 
7.5%
i12819
 
6.5%
l10460
 
5.3%
o10341
 
5.3%
r9821
 
5.0%
t8964
 
4.6%
Other values (39)59182
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter150620
76.8%
Uppercase Letter28034
 
14.3%
Space Separator16432
 
8.4%
Decimal Number842
 
0.4%
Other Punctuation307
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
a19819
13.2%
s17434
11.6%
e16175
10.7%
n14788
9.8%
i12819
8.5%
l10460
6.9%
o10341
 
6.9%
r9821
 
6.5%
t8964
 
6.0%
g3962
 
2.6%
Other values (14)26037
17.3%
ValueCountFrequency (%)
B4016
14.3%
C3959
14.1%
S2691
 
9.6%
D2057
 
7.3%
P1947
 
6.9%
N1655
 
5.9%
L1131
 
4.0%
R1077
 
3.8%
A1033
 
3.7%
O990
 
3.5%
Other values (11)7478
26.7%
ValueCountFrequency (%)
4421
50.0%
9421
50.0%
ValueCountFrequency (%)
16432
100.0%
ValueCountFrequency (%)
.307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin178654
91.0%
Common17581
 
9.0%

Most frequent character per script

ValueCountFrequency (%)
a19819
 
11.1%
s17434
 
9.8%
e16175
 
9.1%
n14788
 
8.3%
i12819
 
7.2%
l10460
 
5.9%
o10341
 
5.8%
r9821
 
5.5%
t8964
 
5.0%
B4016
 
2.2%
Other values (35)54017
30.2%
ValueCountFrequency (%)
16432
93.5%
4421
 
2.4%
9421
 
2.4%
.307
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII196235
100.0%

Most frequent character per block

ValueCountFrequency (%)
a19819
 
10.1%
s17434
 
8.9%
16432
 
8.4%
e16175
 
8.2%
n14788
 
7.5%
i12819
 
6.5%
l10460
 
5.3%
o10341
 
5.3%
r9821
 
5.0%
t8964
 
4.6%
Other values (39)59182
30.2%

team_favorite_id
Categorical

MISSING

Distinct32
Distinct (%)0.3%
Missing2289
Missing (%)19.0%
Memory size94.1 KiB
PIT
 
457
NE
 
446
DEN
 
420
SF
 
417
GB
 
401
Other values (27)
7593 

Length

Max length4
Median length3
Mean length2.788678858
Min length2

Characters and Unicode

Total characters27145
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowATL
3rd rowMIA
4th rowDAL
5th rowPIT
ValueCountFrequency (%)
PIT457
 
3.8%
NE446
 
3.7%
DEN420
 
3.5%
SF417
 
3.5%
GB401
 
3.3%
DAL387
 
3.2%
MIN367
 
3.1%
MIA352
 
2.9%
NO343
 
2.9%
PHI342
 
2.8%
Other values (22)5802
48.3%
(Missing)2289
 
19.0%
2021-04-15T23:38:52.524351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pit457
 
4.7%
ne446
 
4.6%
den420
 
4.3%
sf417
 
4.3%
gb401
 
4.1%
dal387
 
4.0%
min367
 
3.8%
mia352
 
3.6%
no343
 
3.5%
phi342
 
3.5%
Other values (22)5802
59.6%

Most occurring characters

ValueCountFrequency (%)
N3049
11.2%
A2832
 
10.4%
I2742
 
10.1%
L2120
 
7.8%
E1982
 
7.3%
C1788
 
6.6%
T1551
 
5.7%
D1354
 
5.0%
B1200
 
4.4%
R1035
 
3.8%
Other values (13)7492
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27145
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3049
11.2%
A2832
 
10.4%
I2742
 
10.1%
L2120
 
7.8%
E1982
 
7.3%
C1788
 
6.6%
T1551
 
5.7%
D1354
 
5.0%
B1200
 
4.4%
R1035
 
3.8%
Other values (13)7492
27.6%

Most occurring scripts

ValueCountFrequency (%)
Latin27145
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3049
11.2%
A2832
 
10.4%
I2742
 
10.1%
L2120
 
7.8%
E1982
 
7.3%
C1788
 
6.6%
T1551
 
5.7%
D1354
 
5.0%
B1200
 
4.4%
R1035
 
3.8%
Other values (13)7492
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII27145
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3049
11.2%
A2832
 
10.4%
I2742
 
10.1%
L2120
 
7.8%
E1982
 
7.3%
C1788
 
6.6%
T1551
 
5.7%
D1354
 
5.0%
B1200
 
4.4%
R1035
 
3.8%
Other values (13)7492
27.6%

spread_favorite
Real number (ℝ)

MISSING
ZEROS

Distinct47
Distinct (%)0.5%
Missing2289
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean-5.395058558
Minimum-26.5
Maximum0
Zeros133
Zeros (%)1.1%
Memory size94.1 KiB
2021-04-15T23:38:52.630608image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-26.5
5-th percentile-12.5
Q1-7
median-4.5
Q3-3
95-th percentile-1
Maximum0
Range26.5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.430468462
Coefficient of variation (CV)-0.6358537958
Kurtosis1.163080923
Mean-5.395058558
Median Absolute Deviation (MAD)2
Skewness-1.06675187
Sum-52515.5
Variance11.76811387
MonotocityNot monotonic
2021-04-15T23:38:52.757233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
-31492
12.4%
-3.5764
 
6.4%
-7703
 
5.8%
-2.5627
 
5.2%
-6533
 
4.4%
-4530
 
4.4%
-6.5490
 
4.1%
-1472
 
3.9%
-2422
 
3.5%
-4.5342
 
2.8%
Other values (37)3359
27.9%
(Missing)2289
19.0%
ValueCountFrequency (%)
-26.51
< 0.1%
-24.51
< 0.1%
-241
< 0.1%
-22.51
< 0.1%
-21.51
< 0.1%
ValueCountFrequency (%)
0133
 
1.1%
-1472
3.9%
-1.5302
2.5%
-2422
3.5%
-2.5627
5.2%

over_under_line
Categorical

HIGH CARDINALITY
MISSING

Distinct68
Distinct (%)0.7%
Missing2298
Missing (%)19.1%
Memory size94.1 KiB
41
 
564
44
 
511
42
 
507
43
 
487
37
 
474
Other values (63)
7182 

Length

Max length4
Median length2
Mean length2.692133676
Min length1

Characters and Unicode

Total characters26181
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row30
2nd row39
3rd row31
4th row31.5
5th row37
ValueCountFrequency (%)
41564
 
4.7%
44511
 
4.3%
42507
 
4.2%
43487
 
4.1%
37474
 
3.9%
40470
 
3.9%
38451
 
3.8%
39425
 
3.5%
45410
 
3.4%
43.5309
 
2.6%
Other values (58)5117
42.6%
(Missing)2298
19.1%
2021-04-15T23:38:53.002923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41564
 
5.8%
44511
 
5.3%
42507
 
5.2%
43487
 
5.0%
37474
 
4.9%
40470
 
4.9%
38451
 
4.7%
39425
 
4.4%
45410
 
4.2%
43.5309
 
3.2%
Other values (57)5062
52.3%

Most occurring characters

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22733
86.8%
Other Punctuation3393
 
13.0%
Space Separator55
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
46952
30.6%
54941
21.7%
34136
18.2%
71156
 
5.1%
8993
 
4.4%
1989
 
4.4%
6975
 
4.3%
2889
 
3.9%
0860
 
3.8%
9842
 
3.7%
ValueCountFrequency (%)
.3393
100.0%
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26181
100.0%

Most frequent character per script

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII26181
100.0%

Most frequent character per block

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

stadium
Categorical

HIGH CARDINALITY

Distinct101
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Metlife Stadium
 
493
Lambeau Field
 
435
Arrowhead Stadium
 
396
Qualcomm Stadium
 
392
Soldier Field
 
378
Other values (96)
9929 

Length

Max length35
Median length16
Mean length17.45412958
Min length8

Characters and Unicode

Total characters209851
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowOrange Bowl
2nd rowRice Stadium
3rd rowBalboa Stadium
4th rowOrange Bowl
5th rowLambeau Field
ValueCountFrequency (%)
Metlife Stadium493
 
4.1%
Lambeau Field435
 
3.6%
Arrowhead Stadium396
 
3.3%
Qualcomm Stadium392
 
3.3%
Soldier Field378
 
3.1%
Candlestick Park347
 
2.9%
Ralph Wilson Stadium331
 
2.8%
Oakland Coliseum323
 
2.7%
Louisiana Superdome322
 
2.7%
Mile High Stadium272
 
2.3%
Other values (91)8334
69.3%
2021-04-15T23:38:53.236301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium6954
23.4%
field2159
 
7.3%
metlife659
 
2.2%
bank649
 
2.2%
memorial590
 
2.0%
dome565
 
1.9%
coliseum551
 
1.9%
of444
 
1.5%
high438
 
1.5%
mile438
 
1.5%
Other values (129)16304
54.8%

Most occurring characters

ValueCountFrequency (%)
i18965
 
9.0%
17728
 
8.4%
a17204
 
8.2%
e16430
 
7.8%
d12721
 
6.1%
t12689
 
6.0%
m12178
 
5.8%
u11605
 
5.5%
o10141
 
4.8%
l10003
 
4.8%
Other values (45)70187
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter159491
76.0%
Uppercase Letter31317
 
14.9%
Space Separator17728
 
8.4%
Other Punctuation736
 
0.4%
Dash Punctuation245
 
0.1%
Open Punctuation167
 
0.1%
Close Punctuation167
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
S8699
27.8%
F3109
 
9.9%
M2572
 
8.2%
A2196
 
7.0%
C2084
 
6.7%
L1869
 
6.0%
H1720
 
5.5%
B1402
 
4.5%
R1164
 
3.7%
T1107
 
3.5%
Other values (14)5395
17.2%
ValueCountFrequency (%)
i18965
11.9%
a17204
10.8%
e16430
10.3%
d12721
8.0%
t12689
8.0%
m12178
7.6%
u11605
 
7.3%
o10141
 
6.4%
l10003
 
6.3%
n7952
 
5.0%
Other values (14)29603
18.6%
ValueCountFrequency (%)
.297
40.4%
'221
30.0%
&218
29.6%
ValueCountFrequency (%)
17728
100.0%
ValueCountFrequency (%)
-245
100.0%
ValueCountFrequency (%)
(167
100.0%
ValueCountFrequency (%)
)167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin190808
90.9%
Common19043
 
9.1%

Most frequent character per script

ValueCountFrequency (%)
i18965
 
9.9%
a17204
 
9.0%
e16430
 
8.6%
d12721
 
6.7%
t12689
 
6.7%
m12178
 
6.4%
u11605
 
6.1%
o10141
 
5.3%
l10003
 
5.2%
S8699
 
4.6%
Other values (38)60173
31.5%
ValueCountFrequency (%)
17728
93.1%
.297
 
1.6%
-245
 
1.3%
'221
 
1.2%
&218
 
1.1%
(167
 
0.9%
)167
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII209851
100.0%

Most frequent character per block

ValueCountFrequency (%)
i18965
 
9.0%
17728
 
8.4%
a17204
 
8.2%
e16430
 
7.8%
d12721
 
6.1%
t12689
 
6.0%
m12178
 
5.8%
u11605
 
5.5%
o10141
 
4.8%
l10003
 
4.8%
Other values (45)70187
33.4%

address
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct89
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
1 MetLife Stadium Dr, East Rutherford, NJ
 
659
1701 Bryant St, Denver, CO
 
438
1265 Lombardi Ave, Green Bay, WI
 
435
1 Patriot Pl, Foxborough, MA
 
408
1 Arrowhead Dr, Kansas City, MO
 
396
Other values (84)
9687 

Length

Max length87
Median length33
Mean length33.71512934
Min length12

Characters and Unicode

Total characters405357
Distinct characters71
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row1501 NW 3rd St, Miami, FL
2nd row2176 University Boulevard, Houston, TX
3rd rowBalboa Stadium, San Diego, CA
4th row1501 NW 3rd St, Miami, FL
5th row1265 Lombardi Ave, Green Bay, WI
ValueCountFrequency (%)
1 MetLife Stadium Dr, East Rutherford, NJ 659
 
5.5%
1701 Bryant St, Denver, CO 438
 
3.6%
1265 Lombardi Ave, Green Bay, WI 435
 
3.6%
1 Patriot Pl, Foxborough, MA 408
 
3.4%
1 Arrowhead Dr, Kansas City, MO 396
 
3.3%
9449 Friars Rd, San Diego, CA 392
 
3.3%
1410 Museum Campus Dr, Chicago, IL 378
 
3.1%
1 Bills Dr, Orchard Park, NY 372
 
3.1%
1500 Sugar Bowl Dr, New Orleans, LA 358
 
3.0%
490 Jamestown Ave, San Francisco, CA 347
 
2.9%
Other values (79)7840
65.2%
2021-04-15T23:38:53.509521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dr3063
 
4.3%
12950
 
4.2%
st2549
 
3.6%
ave1862
 
2.6%
ca1514
 
2.1%
way1235
 
1.7%
s1205
 
1.7%
east888
 
1.3%
stadium836
 
1.2%
fl822
 
1.2%
Other values (303)53924
76.1%

Most occurring characters

ValueCountFrequency (%)
70511
17.4%
a24256
 
6.0%
,23572
 
5.8%
e21436
 
5.3%
r18948
 
4.7%
t18179
 
4.5%
i15955
 
3.9%
n15219
 
3.8%
o15071
 
3.7%
l12445
 
3.1%
Other values (61)169765
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter205724
50.8%
Uppercase Letter70772
 
17.5%
Space Separator70514
 
17.4%
Decimal Number33747
 
8.3%
Other Punctuation24463
 
6.0%
Dash Punctuation91
 
< 0.1%
Open Punctuation14
 
< 0.1%
Close Punctuation14
 
< 0.1%
Control9
 
< 0.1%
Connector Punctuation9
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A8522
12.0%
S8006
 
11.3%
C5942
 
8.4%
D5445
 
7.7%
M4779
 
6.8%
N3747
 
5.3%
P3603
 
5.1%
L3479
 
4.9%
B3224
 
4.6%
F3167
 
4.5%
Other values (15)20858
29.5%
ValueCountFrequency (%)
a24256
11.8%
e21436
10.4%
r18948
9.2%
t18179
 
8.8%
i15955
 
7.8%
n15219
 
7.4%
o15071
 
7.3%
l12445
 
6.0%
s10394
 
5.1%
d7988
 
3.9%
Other values (14)45833
22.3%
ValueCountFrequency (%)
09877
29.3%
19634
28.5%
23043
 
9.0%
42751
 
8.2%
52140
 
6.3%
92042
 
6.1%
31678
 
5.0%
71261
 
3.7%
8778
 
2.3%
6543
 
1.6%
ValueCountFrequency (%)
,23572
96.4%
.716
 
2.9%
&175
 
0.7%
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
ValueCountFrequency (%)
70511
> 99.9%
 3
 
< 0.1%
ValueCountFrequency (%)
-91
100.0%
ValueCountFrequency (%)
(14
100.0%
ValueCountFrequency (%)
)14
100.0%
ValueCountFrequency (%)
_9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin276496
68.2%
Common128861
31.8%

Most frequent character per script

ValueCountFrequency (%)
a24256
 
8.8%
e21436
 
7.8%
r18948
 
6.9%
t18179
 
6.6%
i15955
 
5.8%
n15219
 
5.5%
o15071
 
5.5%
l12445
 
4.5%
s10394
 
3.8%
A8522
 
3.1%
Other values (39)116071
42.0%
ValueCountFrequency (%)
70511
54.7%
,23572
 
18.3%
09877
 
7.7%
19634
 
7.5%
23043
 
2.4%
42751
 
2.1%
52140
 
1.7%
92042
 
1.6%
31678
 
1.3%
71261
 
1.0%
Other values (12)2352
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII405345
> 99.9%
None12
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
70511
17.4%
a24256
 
6.0%
,23572
 
5.8%
e21436
 
5.3%
r18948
 
4.7%
t18179
 
4.5%
i15955
 
3.9%
n15219
 
3.8%
o15071
 
3.7%
l12445
 
3.1%
Other values (57)169753
41.9%
ValueCountFrequency (%)
3
25.0%
 3
25.0%
3
25.0%
3
25.0%

stadium_neutral
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
False
11973 
True
 
50
ValueCountFrequency (%)
False11973
99.6%
True50
 
0.4%
2021-04-15T23:38:53.594639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

dt_for_home
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct37
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.478348417
Minimum0
Maximum5448.926695
Zeros11973
Zeros (%)99.6%
Memory size94.1 KiB
2021-04-15T23:38:53.660307image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5448.926695
Range5448.926695
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9933308
Coefficient of variation (CV)19.72847194
Kurtosis587.4289345
Mean9.478348417
Median Absolute Deviation (MAD)0
Skewness23.37250991
Sum113958.183
Variance34966.50575
MonotocityNot monotonic
2021-04-15T23:38:53.859422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
011973
99.6%
4263.2319815
 
< 0.1%
58.13918365
 
< 0.1%
5446.3122712
 
< 0.1%
1588.6589942
 
< 0.1%
4428.5277732
 
< 0.1%
5448.9266952
 
< 0.1%
5358.7107872
 
< 0.1%
1887.0713112
 
< 0.1%
1626.725191
 
< 0.1%
Other values (27)27
 
0.2%
ValueCountFrequency (%)
011973
99.6%
8.7442982191
 
< 0.1%
27.676572881
 
< 0.1%
58.13918365
 
< 0.1%
501.9017311
 
< 0.1%
ValueCountFrequency (%)
5448.9266952
< 0.1%
5446.3122712
< 0.1%
5358.7107872
< 0.1%
4441.2057821
< 0.1%
4428.5277732
< 0.1%

dt_for_away
Real number (ℝ≥0)

Distinct1796
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009.313372
Minimum1
Maximum5277.157425
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:53.971588image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile182.5727665
Q1456.6758829
median885.2706156
Q31374.445898
95-th percentile2317.739076
Maximum5277.157425
Range5276.157425
Interquartile range (IQR)917.770015

Descriptive statistics

Standard deviation667.3563109
Coefficient of variation (CV)0.661198325
Kurtosis0.1268528189
Mean1009.313372
Median Absolute Deviation (MAD)449.2808613
Skewness0.7903946828
Sum12134974.67
Variance445364.4457
MonotocityNot monotonic
2021-04-15T23:38:54.093534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258.193156257
 
0.5%
171.277677957
 
0.5%
560.102473855
 
0.5%
293.358936955
 
0.5%
182.572766554
 
0.4%
82.6434508553
 
0.4%
832.330327752
 
0.4%
1234.38861151
 
0.4%
284.968323151
 
0.4%
829.010900251
 
0.4%
Other values (1786)11487
95.5%
ValueCountFrequency (%)
125
0.2%
26.686854372
 
< 0.1%
32.25485441
 
< 0.1%
59.24614161
 
< 0.1%
60.525307251
 
< 0.1%
ValueCountFrequency (%)
5277.1574251
< 0.1%
4850.9940141
< 0.1%
4795.703111
< 0.1%
4628.6532151
< 0.1%
4187.0811331
< 0.1%

bearing_away
Real number (ℝ≥0)

Distinct1800
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.6324059
Minimum0.3504726315
Maximum359.9050725
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:54.218085image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.3504726315
5-th percentile28.3023362
Q177.99553727
median180.8980629
Q3274.1097949
95-th percentile332.0999715
Maximum359.9050725
Range359.5545999
Interquartile range (IQR)196.1142576

Descriptive statistics

Standard deviation103.6007728
Coefficient of variation (CV)0.5832312652
Kurtosis-1.465417147
Mean177.6324059
Median Absolute Deviation (MAD)97.32200666
Skewness0.000302425806
Sum2135674.416
Variance10733.12013
MonotocityNot monotonic
2021-04-15T23:38:54.353156image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.480300157
 
0.5%
55.3338964957
 
0.5%
277.85396955
 
0.5%
302.395472355
 
0.5%
353.210746854
 
0.4%
43.7053000653
 
0.4%
50.9801245152
 
0.4%
22.1096535651
 
0.4%
301.482271351
 
0.4%
238.425960851
 
0.4%
Other values (1790)11487
95.5%
ValueCountFrequency (%)
0.350472631520
0.2%
0.35164382354
 
< 0.1%
0.54232780365
 
< 0.1%
0.5668022928
 
0.1%
0.68698245425
 
< 0.1%
ValueCountFrequency (%)
359.90507251
 
< 0.1%
359.8718142
 
< 0.1%
359.82892788
0.1%
359.76138889
0.1%
359.7599892
 
< 0.1%

bearing_home
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct37
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5719847875
Minimum0
Maximum333.8636947
Zeros11973
Zeros (%)99.6%
Memory size94.1 KiB
2021-04-15T23:38:54.473051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum333.8636947
Range333.8636947
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.05999988
Coefficient of variation (CV)19.33617839
Kurtosis604.5389622
Mean0.5719847875
Median Absolute Deviation (MAD)0
Skewness23.58029354
Sum6876.9731
Variance122.3235974
MonotocityNot monotonic
2021-04-15T23:38:54.581088image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
011973
99.6%
44.401985295
 
< 0.1%
333.86369475
 
< 0.1%
62.434938532
 
< 0.1%
34.083998782
 
< 0.1%
43.00212452
 
< 0.1%
126.01628732
 
< 0.1%
32.602089392
 
< 0.1%
34.17888792
 
< 0.1%
149.06126781
 
< 0.1%
Other values (27)27
 
0.2%
ValueCountFrequency (%)
011973
99.6%
30.289955981
 
< 0.1%
32.602089392
 
< 0.1%
34.083998782
 
< 0.1%
34.17888792
 
< 0.1%
ValueCountFrequency (%)
333.86369475
< 0.1%
287.80859561
 
< 0.1%
280.31782471
 
< 0.1%
273.86187381
 
< 0.1%
268.87264261
 
< 0.1%

compass_away
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
NE
3709 
NW
3262 
SW
2773 
SE
2279 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters24046
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSE
2nd rowSE
3rd rowSW
4th rowSW
5th rowNW
ValueCountFrequency (%)
NE3709
30.8%
NW3262
27.1%
SW2773
23.1%
SE2279
19.0%
2021-04-15T23:38:54.774107image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-15T23:38:54.832798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ne3709
30.8%
nw3262
27.1%
sw2773
23.1%
se2279
19.0%

Most occurring characters

ValueCountFrequency (%)
N6971
29.0%
W6035
25.1%
E5988
24.9%
S5052
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter24046
100.0%

Most frequent character per category

ValueCountFrequency (%)
N6971
29.0%
W6035
25.1%
E5988
24.9%
S5052
21.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24046
100.0%

Most frequent character per script

ValueCountFrequency (%)
N6971
29.0%
W6035
25.1%
E5988
24.9%
S5052
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII24046
100.0%

Most frequent character per block

ValueCountFrequency (%)
N6971
29.0%
W6035
25.1%
E5988
24.9%
S5052
21.0%

compass_home
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)8.0%
Missing11973
Missing (%)99.6%
Memory size94.1 KiB
NE
23 
SE
12 
NW
SW

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters100
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowNE
3rd rowSE
4th rowNE
5th rowSE
ValueCountFrequency (%)
NE23
 
0.2%
SE12
 
0.1%
NW8
 
0.1%
SW7
 
0.1%
(Missing)11973
99.6%
2021-04-15T23:38:55.000466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-15T23:38:55.057528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ne23
46.0%
se12
24.0%
nw8
 
16.0%
sw7
 
14.0%

Most occurring characters

ValueCountFrequency (%)
E35
35.0%
N31
31.0%
S19
19.0%
W15
15.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter100
100.0%

Most frequent character per category

ValueCountFrequency (%)
E35
35.0%
N31
31.0%
S19
19.0%
W15
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100
100.0%

Most frequent character per script

ValueCountFrequency (%)
E35
35.0%
N31
31.0%
S19
19.0%
W15
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ValueCountFrequency (%)
E35
35.0%
N31
31.0%
S19
19.0%
W15
15.0%

team1
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
NE
 
445
PIT
 
443
SF
 
442
DEN
 
441
LVR
 
440
Other values (26)
9812 

Length

Max length3
Median length3
Mean length2.791067121
Min length2

Characters and Unicode

Total characters33557
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMIA
2nd rowTEN
3rd rowLAC
4th rowMIA
5th rowGB
ValueCountFrequency (%)
NE445
 
3.7%
PIT443
 
3.7%
SF442
 
3.7%
DEN441
 
3.7%
LVR440
 
3.7%
MIA437
 
3.6%
GB436
 
3.6%
KC433
 
3.6%
IND431
 
3.6%
MIN431
 
3.6%
Other values (21)7644
63.6%
2021-04-15T23:38:55.244091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne445
 
3.7%
pit443
 
3.7%
sf442
 
3.7%
den441
 
3.7%
lvr440
 
3.7%
mia437
 
3.6%
gb436
 
3.6%
kc433
 
3.6%
ind431
 
3.6%
min431
 
3.6%
Other values (21)7644
63.6%

Most occurring characters

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter33557
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

Most occurring scripts

ValueCountFrequency (%)
Latin33557
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII33557
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3805
11.3%
A3472
 
10.3%
I3353
 
10.0%
L2716
 
8.1%
E2482
 
7.4%
C2304
 
6.9%
T2045
 
6.1%
D1685
 
5.0%
R1457
 
4.3%
B1415
 
4.2%
Other values (13)8823
26.3%

team2
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
TEN
 
441
PIT
 
438
IND
 
437
NE
 
437
GB
 
436
Other values (26)
9834 

Length

Max length3
Median length3
Mean length2.792813774
Min length2

Characters and Unicode

Total characters33578
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLVR
2nd rowDEN
3rd rowBUF
4th rowNYJ
5th rowIND
ValueCountFrequency (%)
TEN441
 
3.7%
PIT438
 
3.6%
IND437
 
3.6%
NE437
 
3.6%
GB436
 
3.6%
NYJ434
 
3.6%
KC434
 
3.6%
LAR433
 
3.6%
MIN432
 
3.6%
LVR431
 
3.6%
Other values (21)7670
63.8%
2021-04-15T23:38:55.450461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ten441
 
3.7%
pit438
 
3.6%
ne437
 
3.6%
ind437
 
3.6%
gb436
 
3.6%
nyj434
 
3.6%
kc434
 
3.6%
lar433
 
3.6%
min432
 
3.6%
buf431
 
3.6%
Other values (21)7670
63.8%

Most occurring characters

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter33578
100.0%

Most frequent character per category

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

Most occurring scripts

ValueCountFrequency (%)
Latin33578
100.0%

Most frequent character per script

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII33578
100.0%

Most frequent character per block

ValueCountFrequency (%)
N3814
11.4%
A3492
 
10.4%
I3344
 
10.0%
L2721
 
8.1%
E2486
 
7.4%
C2305
 
6.9%
T2067
 
6.2%
D1671
 
5.0%
R1466
 
4.4%
B1432
 
4.3%
Other values (13)8780
26.1%

elo1_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11870
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.655702
Minimum1173.652
Maximum1839.663
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:55.660184image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1173.652
5-th percentile1336.1158
Q11434.468425
median1506.741
Q31579.6065
95-th percentile1673.9159
Maximum1839.663
Range666.011
Interquartile range (IQR)145.1380753

Descriptive statistics

Standard deviation102.5346473
Coefficient of variation (CV)0.06805446469
Kurtosis-0.3787149984
Mean1506.655702
Median Absolute Deviation (MAD)72.63
Skewness-0.03185488826
Sum18114521.5
Variance10513.35389
MonotocityNot monotonic
2021-04-15T23:38:55.780473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13007
 
0.1%
1479.2892
 
< 0.1%
1487.3342
 
< 0.1%
1479.9462
 
< 0.1%
1371.3662
 
< 0.1%
1444.9112
 
< 0.1%
1547.2132
 
< 0.1%
1459.9562
 
< 0.1%
1495.2212
 
< 0.1%
1522.7432
 
< 0.1%
Other values (11860)11998
99.8%
ValueCountFrequency (%)
1173.6521
< 0.1%
1175.1361
< 0.1%
1187.5811
< 0.1%
1187.6311
< 0.1%
1197.3011
< 0.1%
ValueCountFrequency (%)
1839.6631
< 0.1%
1831.4621
< 0.1%
1824.2241
< 0.1%
1821.8151
< 0.1%
1810.5021
< 0.1%

elo2_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11853
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1504.906886
Minimum1166.933
Maximum1825.961
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:55.907570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1166.933
5-th percentile1337.68877
Q11435.05418
median1506.595
Q31578.167
95-th percentile1666.933797
Maximum1825.961
Range659.028
Interquartile range (IQR)143.1128197

Descriptive statistics

Standard deviation100.6190123
Coefficient of variation (CV)0.06686062324
Kurtosis-0.4054817953
Mean1504.906886
Median Absolute Deviation (MAD)71.564
Skewness-0.08476790899
Sum18093495.49
Variance10124.18564
MonotocityNot monotonic
2021-04-15T23:38:56.029192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1488.0233
 
< 0.1%
1455.0323
 
< 0.1%
1552.3693
 
< 0.1%
13003
 
< 0.1%
1573.1182
 
< 0.1%
1546.1322
 
< 0.1%
1439.1412
 
< 0.1%
1544.8132
 
< 0.1%
1474.8882
 
< 0.1%
1456.5832
 
< 0.1%
Other values (11843)11999
99.8%
ValueCountFrequency (%)
1166.9331
< 0.1%
1175.391
< 0.1%
1177.5171
< 0.1%
1193.0921
< 0.1%
1201.5614631
< 0.1%
ValueCountFrequency (%)
1825.9611
< 0.1%
1809.4391
< 0.1%
1806.3461
< 0.1%
1785.3921
< 0.1%
1783.6351
< 0.1%

elo_prob1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11948
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5830011673
Minimum0.0709532918
Maximum0.9705164087
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:56.160404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0709532918
5-th percentile0.275758963
Q10.4613663876
median0.5941427535
Q30.7148561061
95-th percentile0.847058109
Maximum0.9705164087
Range0.8995631169
Interquartile range (IQR)0.2534897185

Descriptive statistics

Standard deviation0.1730944016
Coefficient of variation (CV)0.2969023242
Kurtosis-0.6106302655
Mean0.5830011673
Median Absolute Deviation (MAD)0.1262872004
Skewness-0.2676488247
Sum7009.423034
Variance0.02996167185
MonotocityNot monotonic
2021-04-15T23:38:56.277127image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44020587612
 
< 0.1%
0.68798203612
 
< 0.1%
0.54853913042
 
< 0.1%
0.52002752572
 
< 0.1%
0.43939747082
 
< 0.1%
0.59845097332
 
< 0.1%
0.4556310692
 
< 0.1%
0.56073865012
 
< 0.1%
0.457384932
 
< 0.1%
0.73567936242
 
< 0.1%
Other values (11938)12003
99.8%
ValueCountFrequency (%)
0.07095329181
< 0.1%
0.10374969241
< 0.1%
0.10599817881
< 0.1%
0.10632428181
< 0.1%
0.10750878421
< 0.1%
ValueCountFrequency (%)
0.97051640871
< 0.1%
0.96457805711
< 0.1%
0.96214250271
< 0.1%
0.95675549171
< 0.1%
0.95656120561
< 0.1%

elo_prob2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11948
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4169988327
Minimum0.02948359131
Maximum0.9290467082
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:56.400718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.02948359131
5-th percentile0.152941891
Q10.2851438939
median0.4058572465
Q30.5386336124
95-th percentile0.724241037
Maximum0.9290467082
Range0.8995631169
Interquartile range (IQR)0.2534897185

Descriptive statistics

Standard deviation0.1730944016
Coefficient of variation (CV)0.4150956501
Kurtosis-0.6106302655
Mean0.4169988327
Median Absolute Deviation (MAD)0.1262872004
Skewness0.2676488247
Sum5013.576966
Variance0.02996167185
MonotocityNot monotonic
2021-04-15T23:38:56.516837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17798882022
 
< 0.1%
0.40249143062
 
< 0.1%
0.48686389742
 
< 0.1%
0.23395374182
 
< 0.1%
0.43926134992
 
< 0.1%
0.32201825912
 
< 0.1%
0.33008190072
 
< 0.1%
0.16372787362
 
< 0.1%
0.4920438012
 
< 0.1%
0.36172064552
 
< 0.1%
Other values (11938)12003
99.8%
ValueCountFrequency (%)
0.029483591311
< 0.1%
0.035421942851
< 0.1%
0.03785749731
< 0.1%
0.043244508331
< 0.1%
0.043438794421
< 0.1%
ValueCountFrequency (%)
0.92904670821
< 0.1%
0.89625030761
< 0.1%
0.89400182121
< 0.1%
0.89367571821
< 0.1%
0.89249121581
< 0.1%

qbelo1_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12019
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1505.973798
Minimum1171.971131
Maximum1806.39016
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:56.638686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1171.971131
5-th percentile1339.577012
Q11436.21795
median1507.532178
Q31575.955952
95-th percentile1668.044963
Maximum1806.39016
Range634.4190289
Interquartile range (IQR)139.7380022

Descriptive statistics

Standard deviation99.3006146
Coefficient of variation (CV)0.06593781027
Kurtosis-0.3780561135
Mean1505.973798
Median Absolute Deviation (MAD)70.10627504
Skewness-0.0539676641
Sum18106322.98
Variance9860.61206
MonotocityNot monotonic
2021-04-15T23:38:56.757853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13005
 
< 0.1%
1462.3892251
 
< 0.1%
1451.7512811
 
< 0.1%
1442.4427971
 
< 0.1%
1426.7027411
 
< 0.1%
1585.2830041
 
< 0.1%
1365.2014391
 
< 0.1%
1379.244831
 
< 0.1%
1564.8035181
 
< 0.1%
1548.842591
 
< 0.1%
Other values (12009)12009
99.9%
ValueCountFrequency (%)
1171.9711311
< 0.1%
1181.149771
< 0.1%
1187.0288031
< 0.1%
1194.5283531
< 0.1%
1198.2290251
< 0.1%
ValueCountFrequency (%)
1806.390161
< 0.1%
1800.2565921
< 0.1%
1793.9137211
< 0.1%
1792.0622231
< 0.1%
1789.8741631
< 0.1%

qbelo2_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12021
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1504.672397
Minimum1164.327676
Maximum1798.835806
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:56.882390image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1164.327676
5-th percentile1341.43159
Q11436.391035
median1506.790498
Q31575.512407
95-th percentile1660.35048
Maximum1798.835806
Range634.5081305
Interquartile range (IQR)139.1213717

Descriptive statistics

Standard deviation97.44907196
Coefficient of variation (CV)0.06476431159
Kurtosis-0.3859660852
Mean1504.672397
Median Absolute Deviation (MAD)69.35023294
Skewness-0.1073823444
Sum18090676.23
Variance9496.321626
MonotocityNot monotonic
2021-04-15T23:38:57.001695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13003
 
< 0.1%
1610.3455551
 
< 0.1%
1476.6197481
 
< 0.1%
1517.4267571
 
< 0.1%
1610.5386431
 
< 0.1%
1415.1848741
 
< 0.1%
1662.6249121
 
< 0.1%
1528.7301061
 
< 0.1%
1541.1409691
 
< 0.1%
1457.1581931
 
< 0.1%
Other values (12011)12011
99.9%
ValueCountFrequency (%)
1164.3276761
< 0.1%
1183.6061811
< 0.1%
1185.5903481
< 0.1%
1194.5028351
< 0.1%
1206.1741131
< 0.1%
ValueCountFrequency (%)
1798.8358061
< 0.1%
1795.5140491
< 0.1%
1783.2193491
< 0.1%
1783.0446491
< 0.1%
1779.0112931
< 0.1%

qb1
Categorical

HIGH CARDINALITY

Distinct527
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Tom Brady
 
175
Brett Favre
 
159
Drew Brees
 
149
Peyton Manning
 
148
John Elway
 
128
Other values (522)
11264 

Length

Max length18
Median length12
Mean length11.92980121
Min length7

Characters and Unicode

Total characters143432
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.5%

Sample

1st rowDick Wood
2nd rowGeorge Blanda
3rd rowJohn Hadl
4th rowRick Norton
5th rowBart Starr
ValueCountFrequency (%)
Tom Brady175
 
1.5%
Brett Favre159
 
1.3%
Drew Brees149
 
1.2%
Peyton Manning148
 
1.2%
John Elway128
 
1.1%
Dan Marino128
 
1.1%
Ben Roethlisberger123
 
1.0%
Philip Rivers122
 
1.0%
Eli Manning108
 
0.9%
Vinny Testaverde107
 
0.9%
Other values (517)10676
88.8%
2021-04-15T23:38:57.350365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
steve543
 
2.3%
jim503
 
2.1%
joe418
 
1.7%
manning325
 
1.3%
matt319
 
1.3%
dan290
 
1.2%
john285
 
1.2%
ryan273
 
1.1%
drew262
 
1.1%
jeff230
 
1.0%
Other values (702)20642
85.7%

Most occurring characters

ValueCountFrequency (%)
e13876
 
9.7%
12067
 
8.4%
a10881
 
7.6%
n10752
 
7.5%
r10716
 
7.5%
o8814
 
6.1%
i7518
 
5.2%
t6052
 
4.2%
l5996
 
4.2%
s4732
 
3.3%
Other values (43)52028
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter106503
74.3%
Uppercase Letter24654
 
17.2%
Space Separator12067
 
8.4%
Other Punctuation208
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
B2725
11.1%
J2580
10.5%
M2499
 
10.1%
D2027
 
8.2%
S1681
 
6.8%
C1505
 
6.1%
T1479
 
6.0%
R1450
 
5.9%
K1247
 
5.1%
P1062
 
4.3%
Other values (15)6399
26.0%
ValueCountFrequency (%)
e13876
13.0%
a10881
10.2%
n10752
10.1%
r10716
10.1%
o8814
 
8.3%
i7518
 
7.1%
t6052
 
5.7%
l5996
 
5.6%
s4732
 
4.4%
h3110
 
2.9%
Other values (15)24056
22.6%
ValueCountFrequency (%)
'113
54.3%
.95
45.7%
ValueCountFrequency (%)
12067
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin131157
91.4%
Common12275
 
8.6%

Most frequent character per script

ValueCountFrequency (%)
e13876
 
10.6%
a10881
 
8.3%
n10752
 
8.2%
r10716
 
8.2%
o8814
 
6.7%
i7518
 
5.7%
t6052
 
4.6%
l5996
 
4.6%
s4732
 
3.6%
h3110
 
2.4%
Other values (40)48710
37.1%
ValueCountFrequency (%)
12067
98.3%
'113
 
0.9%
.95
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII143432
100.0%

Most frequent character per block

ValueCountFrequency (%)
e13876
 
9.7%
12067
 
8.4%
a10881
 
7.6%
n10752
 
7.5%
r10716
 
7.5%
o8814
 
6.1%
i7518
 
5.2%
t6052
 
4.2%
l5996
 
4.2%
s4732
 
3.3%
Other values (43)52028
36.3%

qb2
Categorical

HIGH CARDINALITY

Distinct540
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
Tom Brady
 
158
Brett Favre
 
157
Drew Brees
 
144
Peyton Manning
 
138
Philip Rivers
 
126
Other values (535)
11300 

Length

Max length18
Median length12
Mean length11.94352491
Min length7

Characters and Unicode

Total characters143597
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)0.5%

Sample

1st rowCotton Davidson
2nd rowMickey Slaughter
3rd rowJack Kemp
4th rowMike Taliaferro
5th rowJohnny Unitas
ValueCountFrequency (%)
Tom Brady158
 
1.3%
Brett Favre157
 
1.3%
Drew Brees144
 
1.2%
Peyton Manning138
 
1.1%
Philip Rivers126
 
1.0%
Dan Marino125
 
1.0%
Ben Roethlisberger121
 
1.0%
John Elway120
 
1.0%
Vinny Testaverde107
 
0.9%
Warren Moon106
 
0.9%
Other values (530)10721
89.2%
2021-04-15T23:38:57.599655image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
steve563
 
2.3%
jim508
 
2.1%
joe418
 
1.7%
matt335
 
1.4%
manning308
 
1.3%
dan284
 
1.2%
john282
 
1.2%
ryan279
 
1.2%
drew260
 
1.1%
jeff246
 
1.0%
Other values (708)20610
85.5%

Most occurring characters

ValueCountFrequency (%)
e13993
 
9.7%
12070
 
8.4%
a10874
 
7.6%
r10750
 
7.5%
n10650
 
7.4%
o8829
 
6.1%
i7497
 
5.2%
l6105
 
4.3%
t6037
 
4.2%
s4775
 
3.3%
Other values (44)52017
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter106678
74.3%
Uppercase Letter24646
 
17.2%
Space Separator12070
 
8.4%
Other Punctuation203
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e13993
13.1%
a10874
10.2%
r10750
10.1%
n10650
10.0%
o8829
 
8.3%
i7497
 
7.0%
l6105
 
5.7%
t6037
 
5.7%
s4775
 
4.5%
h3047
 
2.9%
Other values (16)24121
22.6%
ValueCountFrequency (%)
B2711
11.0%
J2587
10.5%
M2506
 
10.2%
D2020
 
8.2%
S1699
 
6.9%
C1505
 
6.1%
R1437
 
5.8%
T1422
 
5.8%
K1258
 
5.1%
P1034
 
4.2%
Other values (15)6467
26.2%
ValueCountFrequency (%)
'109
53.7%
.94
46.3%
ValueCountFrequency (%)
12070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin131324
91.5%
Common12273
 
8.5%

Most frequent character per script

ValueCountFrequency (%)
e13993
 
10.7%
a10874
 
8.3%
r10750
 
8.2%
n10650
 
8.1%
o8829
 
6.7%
i7497
 
5.7%
l6105
 
4.6%
t6037
 
4.6%
s4775
 
3.6%
h3047
 
2.3%
Other values (41)48767
37.1%
ValueCountFrequency (%)
12070
98.3%
'109
 
0.9%
.94
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII143597
100.0%

Most frequent character per block

ValueCountFrequency (%)
e13993
 
9.7%
12070
 
8.4%
a10874
 
7.6%
r10750
 
7.5%
n10650
 
7.4%
o8829
 
6.1%
i7497
 
5.2%
l6105
 
4.3%
t6037
 
4.2%
s4775
 
3.3%
Other values (44)52017
36.2%

qb1_value_pre
Real number (ℝ)

Distinct11939
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.6179557
Minimum-53.77891723
Maximum317.472758
Zeros63
Zeros (%)0.5%
Memory size94.1 KiB
2021-04-15T23:38:57.721827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-53.77891723
5-th percentile12.95774652
Q155.95839186
median92.94148547
Q3134.9675307
95-th percentile205.3281033
Maximum317.472758
Range371.2516753
Interquartile range (IQR)79.00913889

Descriptive statistics

Standard deviation57.96622723
Coefficient of variation (CV)0.5877857315
Kurtosis0.1169659131
Mean98.6179557
Median Absolute Deviation (MAD)39.12280707
Skewness0.5284250014
Sum1185683.681
Variance3360.0835
MonotocityNot monotonic
2021-04-15T23:38:57.847046image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
 
0.5%
112.901587
 
0.1%
103.379764
 
< 0.1%
111.994742
 
< 0.1%
52.14332
 
< 0.1%
28.112042
 
< 0.1%
16.323122
 
< 0.1%
39.382765712
 
< 0.1%
32.646242
 
< 0.1%
98.845562
 
< 0.1%
Other values (11929)11935
99.3%
ValueCountFrequency (%)
-53.778917231
< 0.1%
-46.720907761
< 0.1%
-46.329533031
< 0.1%
-44.98544371
< 0.1%
-44.032285791
< 0.1%
ValueCountFrequency (%)
317.4727581
< 0.1%
313.82838351
< 0.1%
308.7623811
< 0.1%
306.84413241
< 0.1%
305.25446191
< 0.1%

qb2_value_pre
Real number (ℝ)

Distinct11957
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.61372831
Minimum-45.31072334
Maximum327.7165449
Zeros56
Zeros (%)0.5%
Memory size94.1 KiB
2021-04-15T23:38:57.977533image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-45.31072334
5-th percentile13.49364064
Q156.77725953
median92.7051925
Q3134.3100929
95-th percentile203.3585495
Maximum327.7165449
Range373.0272682
Interquartile range (IQR)77.53283338

Descriptive statistics

Standard deviation57.43594259
Coefficient of variation (CV)0.5824335372
Kurtosis0.1078267464
Mean98.61372831
Median Absolute Deviation (MAD)38.44662958
Skewness0.5261227015
Sum1185632.855
Variance3298.887502
MonotocityNot monotonic
2021-04-15T23:38:58.111728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
056
 
0.5%
112.901585
 
< 0.1%
16.128797142
 
< 0.1%
23.124422
 
< 0.1%
74.81432
 
< 0.1%
68.0132
 
< 0.1%
108.82082
 
< 0.1%
61.289429142
 
< 0.1%
111.994742
 
< 0.1%
20.410135821
 
< 0.1%
Other values (11947)11947
99.4%
ValueCountFrequency (%)
-45.310723341
< 0.1%
-36.567947131
< 0.1%
-34.949901641
< 0.1%
-33.932203311
< 0.1%
-33.116684121
< 0.1%
ValueCountFrequency (%)
327.71654491
< 0.1%
310.1306781
< 0.1%
307.03426451
< 0.1%
300.76185811
< 0.1%
300.23408331
< 0.1%

qbelo_prob1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct12023
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5752998104
Minimum0.05981049406
Maximum0.9671966037
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:58.254723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.05981049406
5-th percentile0.2635571972
Q10.447327874
median0.5870829808
Q30.7137568362
95-th percentile0.8476907825
Maximum0.9671966037
Range0.9073861096
Interquartile range (IQR)0.2664289623

Descriptive statistics

Standard deviation0.1780915125
Coefficient of variation (CV)0.3095629605
Kurtosis-0.6398134337
Mean0.5752998104
Median Absolute Deviation (MAD)0.1321854506
Skewness-0.2500567872
Sum6916.829621
Variance0.03171658682
MonotocityNot monotonic
2021-04-15T23:38:58.379105image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.73117730551
 
< 0.1%
0.79112806841
 
< 0.1%
0.57111149161
 
< 0.1%
0.38069452781
 
< 0.1%
0.3367343511
 
< 0.1%
0.73897045521
 
< 0.1%
0.56002514361
 
< 0.1%
0.67107542271
 
< 0.1%
0.33425777721
 
< 0.1%
0.55992988851
 
< 0.1%
Other values (12013)12013
99.9%
ValueCountFrequency (%)
0.059810494061
< 0.1%
0.070228847641
< 0.1%
0.086213926911
< 0.1%
0.094097732631
< 0.1%
0.096356313541
< 0.1%
ValueCountFrequency (%)
0.96719660371
< 0.1%
0.96590984211
< 0.1%
0.96452923611
< 0.1%
0.95932450341
< 0.1%
0.95920375171
< 0.1%

qbelo_prob2
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct12023
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4247001896
Minimum0.03280339633
Maximum0.9401895059
Zeros0
Zeros (%)0.0%
Memory size94.1 KiB
2021-04-15T23:38:58.509358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.03280339633
5-th percentile0.1523092175
Q10.2862431638
median0.4129170192
Q30.552672126
95-th percentile0.7364428028
Maximum0.9401895059
Range0.9073861096
Interquartile range (IQR)0.2664289623

Descriptive statistics

Standard deviation0.1780915125
Coefficient of variation (CV)0.4193346668
Kurtosis-0.6398134337
Mean0.4247001896
Median Absolute Deviation (MAD)0.1321854506
Skewness0.2500567872
Sum5106.170379
Variance0.03171658682
MonotocityNot monotonic
2021-04-15T23:38:58.631448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.76682454391
 
< 0.1%
0.31684668291
 
< 0.1%
0.58565147611
 
< 0.1%
0.17571887721
 
< 0.1%
0.46036732791
 
< 0.1%
0.5617242441
 
< 0.1%
0.21810955291
 
< 0.1%
0.57604832571
 
< 0.1%
0.76087983121
 
< 0.1%
0.59560378531
 
< 0.1%
Other values (12013)12013
99.9%
ValueCountFrequency (%)
0.032803396331
< 0.1%
0.034090157931
< 0.1%
0.035470763911
< 0.1%
0.040675496571
< 0.1%
0.040796248331
< 0.1%
ValueCountFrequency (%)
0.94018950591
< 0.1%
0.92977115241
< 0.1%
0.91378607311
< 0.1%
0.90590226741
< 0.1%
0.90364368651
< 0.1%

home_fav
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.1 KiB
1
6514 
0
5509 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12023
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
16514
54.2%
05509
45.8%
2021-04-15T23:38:58.813710image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-15T23:38:58.869200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
16514
54.2%
05509
45.8%

Most occurring characters

ValueCountFrequency (%)
16514
54.2%
05509
45.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12023
100.0%

Most frequent character per category

ValueCountFrequency (%)
16514
54.2%
05509
45.8%

Most occurring scripts

ValueCountFrequency (%)
Common12023
100.0%

Most frequent character per script

ValueCountFrequency (%)
16514
54.2%
05509
45.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII12023
100.0%

Most frequent character per block

ValueCountFrequency (%)
16514
54.2%
05509
45.8%

Interactions

2021-04-15T23:38:14.268342image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.372411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.477565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.570874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.668682image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.763349image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.867901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:14.971273image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.071621image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.171580image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.267286image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.361999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.462248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.559733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.660558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.847980image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:15.942922image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.039039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.140035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.242654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.344598image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.444051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.544564image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.652923image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.762744image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.868485image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:16.971448image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.071803image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.169014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.268973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.370479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.473953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.576758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.672253image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.767709image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.873479image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:17.975345image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.071512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.253502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.352193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.452635image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.553900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.654120image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.755848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.854342image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:18.952333image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.055678image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.156257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.258310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.359687image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.455930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.553053image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.657185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.762989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.863910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:19.962229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.063706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.167484image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.269276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.372292image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.473971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.653782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.748142image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.847927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:20.945838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.047581image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.149100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.242506image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.335245image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.430738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.525936image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.621073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.712960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.806490image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:21.903564image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.001037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.097534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.195734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.287639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.378924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.475452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.571128image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.669590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.768883image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:22.946996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.038650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.135095image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.231930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.326535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.420878image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.515469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.613980image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.711925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.814523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:23.914488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.012770image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.111083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.209102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.306322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.405136image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.503370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.595592image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.687308image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.788376image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.893029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:24.994403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-15T23:38:25.096616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-04-15T23:38:46.304907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-15T23:38:59.045731image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-15T23:38:59.269665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-15T23:38:59.500165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-15T23:38:59.767014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-15T23:39:00.079832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-15T23:38:46.634131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-15T23:38:47.669617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-15T23:38:48.038989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-15T23:38:48.204594image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

game_iddate_stringschedule_dateschedule_seasonschedule_weekschedule_playoffhome_team_idhome_cityhome_teamnameaway_cityaway_teamnameaway_team_idresultteam_homescore_homescore_awayteam_awayteam_favorite_idspread_favoriteover_under_linestadiumaddressstadium_neutraldt_for_homedt_for_awaybearing_awaybearing_homecompass_awaycompass_hometeam1team2elo1_preelo2_preelo_prob1elo_prob2qbelo1_preqbelo2_preqb1qb2qb1_value_preqb2_value_preqbelo_prob1qbelo_prob2home_fav
019660902MIALVRFri Sep 02, 19661966-09-0219661FalseMIAMiamiDolphinsOaklandRaidersLVR0Miami Dolphins1423Oakland RaidersNaNNaNNaNOrange Bowl1501 NW 3rd St, Miami, FLFalse0.02585.13161996.1866800.0SENaNMIALVR1300.0001523.3650.2866680.7133321300.0000001523.320809Dick WoodCotton Davidson73.28297389.7645730.2675530.7324470
119660903TENDENSat Sep 03, 19661966-09-0319661FalseTENHoustonOilersDenverBroncosDEN1Houston Oilers457Denver BroncosNaNNaNNaNRice Stadium2176 University Boulevard, Houston, TXFalse0.0878.827429138.9009770.0SENaNTENDEN1377.0301382.6900.5845760.4154241376.3044951383.369283George BlandaMickey Slaughter33.81612149.2383030.5688250.4311750
219660904LACBUFSun Sep 04, 19661966-09-0419661FalseLACSan DiegoChargersBuffaloBillsBUF1San Diego Chargers277Buffalo BillsNaNNaNNaNBalboa StadiumBalboa Stadium, San Diego, CAFalse0.02186.916535264.2165760.0SWNaNLACBUF1542.6531619.0020.4836730.5163271539.4884561621.746408John HadlJack Kemp101.31007979.0309500.4774640.5225360
319660909MIANYJFri Sep 09, 19661966-09-0919662FalseMIAMiamiDolphinsNew YorkJetsNYJ0Miami Dolphins1419New York JetsNaNNaNNaNOrange Bowl1501 NW 3rd St, Miami, FLFalse0.01089.663416201.0541870.0SWNaNMIANYJ1287.6851453.6810.3586150.6413851288.5863761452.606478Rick NortonMike Taliaferro0.000000-1.9283810.2588410.7411590
419660910GBINDSat Sep 10, 19661966-09-1019661FalseGBGreen BayPackersBaltimoreColtsIND1Green Bay Packers243Baltimore ColtsNaNNaNNaNLambeau Field1265 Lombardi Ave, Green Bay, WIFalse0.0690.339640305.0826700.0NWNaNGBIND1617.7251586.7510.6347090.3652911606.8816561608.973916Bart StarrJohnny Unitas92.009419162.3444260.5585340.4414660
519660910TENLVRSat Sep 10, 19661966-09-1019662FalseTENHoustonOilersOaklandRaidersLVR1Houston Oilers310Oakland RaidersNaNNaNNaNRice Stadium2176 University Boulevard, Houston, TXFalse0.01636.715849101.8975300.0SENaNTENLVR1406.6691535.6810.4089080.5910921407.2208701534.734433George BlandaCotton Davidson53.56399465.3441160.4256780.5743220
619660910LACNESat Sep 10, 19661966-09-1019662FalseLACSan DiegoChargersFoxboroughPatriotsNE1San Diego Chargers240New England PatriotsNaNNaNNaNBalboa StadiumBalboa Stadium, San Diego, CAFalse0.02573.968470270.9170600.0NWNaNLACNE1574.2551475.3320.7198300.2801701571.5341461480.182564John HadlBabe Parilli114.24143297.4185120.6889490.3110510
719660911ATLLARSun Sep 11, 19661966-09-1119661FalseATLAtlantaFalconsLos AngelesRamsLAR0Atlanta Falcons1419Los Angeles RamsNaNNaNNaNAtlanta-Fulton County Stadium521 Capitol Avenue SE, Atlanta, GeorgiaFalse0.01936.88066980.9805280.0NENaNATLLAR1300.0001476.2160.3451990.6548011300.0000001460.937834Randy JohnsonRoman Gabriel0.000000121.3488990.2634070.7365930
819660911BUFKCSun Sep 11, 19661966-09-1119662FalseBUFBuffaloBillsKansas CityChiefsKC0Buffalo Bills2042Kansas City ChiefsNaNNaNNaNWar Memorial Stadium285 Dodge Street, Buffalo, NYFalse0.0861.10686467.2066450.0NENaNBUFKC1587.4001522.2690.6789810.3210191589.7007191519.003455Jack KempLen Dawson54.105899148.6649800.6193470.3806530
919660911DETCHISun Sep 11, 19661966-09-1119661FalseDETDetroitLionsChicagoBearsCHI1Detroit Lions143Chicago BearsNaNNaNNaNTiger Stadium2121 Trumbull Ave., Detroit, MIFalse0.0364.99957995.4335080.0SENaNDETCHI1491.6801591.9080.4494760.5505241503.1172321589.035709Milt PlumRudy Bukich54.955723111.8847080.4481720.5518280

Last rows

game_iddate_stringschedule_dateschedule_seasonschedule_weekschedule_playoffhome_team_idhome_cityhome_teamnameaway_cityaway_teamnameaway_team_idresultteam_homescore_homescore_awayteam_awayteam_favorite_idspread_favoriteover_under_linestadiumaddressstadium_neutraldt_for_homedt_for_awaybearing_awaybearing_homecompass_awaycompass_hometeam1team2elo1_preelo2_preelo_prob1elo_prob2qbelo1_preqbelo2_preqb1qb2qb1_value_preqb2_value_preqbelo_prob1qbelo_prob2home_fav
1201320210110NOCHISun Jan 10, 20212021-01-102020WildcardTrueNONew OrleansSaintsChicagoBearsCHI1New Orleans Saints219Chicago BearsNO-11.048Mercedes-Benz Superdome1500 Sugar Bowl Dr, New Orleans, LAFalse0.0833.524038190.2612340.0SWNaNNOCHI1695.6835991500.1184650.8175650.1824351730.5346381497.160456Drew BreesMitchell Trubisky222.286925164.8393310.8498200.1501801
1201420210110PITCLESun Jan 10, 20212021-01-102020WildcardTruePITPittsburghSteelersClevelandBrownsCLE0Pittsburgh Steelers3748Cleveland BrownsPIT-5.547.5Heinz Field100 Art Rooney Ave, Pittsburgh, PAFalse0.0114.228939129.1938270.0SENaNPITCLE1572.1614421516.9817690.6663690.3336311578.4457221536.927531Ben RoethlisbergerBaker Mayfield204.930263164.4893120.6194430.3805571
1201520210110TENBALSun Jan 10, 20212021-01-102020WildcardTrueTENTennesseeTitansBaltimoreRavensBAL0Tennessee Titans1320Baltimore RavensBAL-3.553.5Nissan Stadium1 Titans Way, Nashville, TNFalse0.0596.492606251.9921330.0SWNaNTENBAL1599.0765991654.2150040.5141880.4858121571.6963931651.052726Ryan TannehillLamar Jackson216.955557249.5656730.4257140.5742860
1201620210116BUFBALSat Jan 16, 20212021-01-162020DivisionTrueBUFBuffaloBillsBaltimoreRavensBAL1Buffalo Bills173Baltimore RavensBUF-2.549.5New Era Field1 Bills Dr, Orchard Park, NYFalse0.0265.725548335.3828930.0NWNaNBUFBAL1700.5380091675.6957770.6264870.3735131688.2529861668.348824Josh AllenLamar Jackson289.086698243.1852250.6528860.3471141
1201720210116GBLARSat Jan 16, 20212021-01-162020DivisionTrueGBGreen BayPackersLos AngelesRamsLAR1Green Bay Packers3218Los Angeles RamsGB-7.045Lambeau Field1265 Lombardi Ave, Green Bay, WIFalse0.01758.30052656.7915650.0NENaNGBLAR1700.2260621620.4985350.6970140.3029861674.7860931633.700632Aaron RodgersJared Goff266.649955161.6745290.7108750.2891251
1201820210117KCCLESun Jan 17, 20212021-01-172020DivisionTrueKCKansas CityChiefsClevelandBrownsCLE1Kansas City Chiefs2217Cleveland BrownsKC-8.056Arrowhead Stadium1 Arrowhead Dr, Kansas City, MOFalse0.0696.123935260.1274970.0SWNaNKCCLE1712.6520901552.0127250.7856470.2143531711.2170291568.944343Patrick MahomesBaker Mayfield273.850726182.7177660.7899270.2100731
1201920210117NOTBSun Jan 17, 20212021-01-172020DivisionTrueNONew OrleansSaintsTampa BayBuccaneersTB0New Orleans Saints2030Tampa Bay BuccaneersNO-2.553Mercedes-Benz Superdome1500 Sugar Bowl Dr, New Orleans, LAFalse0.0481.270463290.6705060.0NWNaNNOTB1704.0512941645.0740080.6712120.3287881737.3112961624.424406Drew BreesTom Brady226.770860221.7779060.7058590.2941411
1202020210124GBTBSun Jan 24, 20212021-01-242020ConferenceTrueGBGreen BayPackersTampa BayBuccaneersTB0Green Bay Packers2631Tampa Bay BuccaneersGB-3.053Lambeau Field1265 Lombardi Ave, Green Bay, WIFalse0.01198.450281346.7346340.0NWNaNGBTB1715.6231871679.1862550.6419680.3580321689.4067921660.789489Aaron RodgersTom Brady276.653962221.7470910.6291910.3708091
1202120210124KCBUFSun Jan 24, 20212021-01-242020ConferenceTrueKCKansas CityChiefsBuffaloBillsBUF1Kansas City Chiefs3824Buffalo BillsKC-3.055Arrowhead Stadium1 Arrowhead Dr, Kansas City, MOFalse0.0856.619992257.2687830.0SWNaNKCBUF1719.6189211719.9741490.5919720.4080281718.0322471706.159766Patrick MahomesJosh Allen271.760879273.2900460.5356370.4643631
1202220210207TBKCSun Feb 07, 20212021-02-072020SuperbowlTrueTBTampa BayBuccaneersKansas CityChiefsKC1Tampa Bay Buccaneers319Kansas City ChiefsKC-3.056Raymond James Stadium4201 N Dale Mabry Hwy, Tampa, FLFalse0.01033.075041134.1819580.0SENaNTBKC1703.3032961741.0872790.5390870.4609131684.3190581742.902172Tom BradyPatrick Mahomes211.680227282.2613260.4662060.5337940